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Stakeholder engagement in agro-climate service planning

1. Stakeholder engagement approach in agro-climate service scaling

This document describes the approach to engaging stakeholders in generating and translating systems knowledge into decision-making. The approach is a combination of decision analysis, stakeholder identification and stakeholder analysis.

We applied our approach to the case of Dien Bien, Vietnam, for which we generated scientific evidence on agro-climate services (ACS) scaling. We further make suggestions on how stakeholders can be engaged in integrating scientific evidence into socio-economic development planning in Dien Bien.

The full procedure for integrating stakeholder engagement and stakeholder analysis with the decision analysis approach is available in Repository: Stakeholder-Decision-Analysis-ACS.

1.1. Knowledge generation and knowledge integration approach

We combined decision analysis, stakeholder identification and stakeholder analysis approaches. We provide details on all methodological steps in the Figure below

Approach to engage stakeholders in agro-climate service planning. The methodological steps are further developed based on the decision analysis procedure used in the study by Luu et al. (2022).

1.2. Identification of stakeholders

We defined a stakeholder as “any individual or group who has an interest in a decision or who can affect or is affected by a decision”.

This definition, combined with various tools and techniques (see below Figure) helped us to identify the key stakeholders of the decisions to invest in scaling ACS.

Figure: Methods, tools and techniques used to identify stakeholders of the potential decisions to invest in agro-climate services in Dien Bien District, Vietnam. Adapted from Reed et al. (2009)

The List of targeted stakeholders contains the list of key stakeholders for potential engagement in knowledge generation and translation, aiming to influence agro-climate service investment decision-making.

1.3. Identification of experts

Once stakeholders had been identified, we used another set of criteria (i.e. based on stakeholders’ experience, availability, expertise and gender) to select potential experts who should be invited to join the ACS knowledge generation process.

For this purpose, we grouped stakeholders based on their time availability and experience into core experts and resource persons. Together with CVN’s technical working group, we evaluated the availability and expertise of each key stakeholder by scoring them on a scale from 0 to 5 for these traits. The score helped to select experts based on the following criteria.

• Core experts: Availability score > 2.5 and experience related to different aspects in ACS scaling > 2.5
• Resource persons: Availability score ≤ 2.5 and experience related to different aspects in ACS scaling > 2.5

We considered the gender of stakeholders to support the constitution of a gender-balanced team of experts. Furthermore, we mapped out the expertise (i.e. knowledge and skills related to ACS) of stakeholders to help us identify experts with representative expertise across the value chain.

We used the ggplot2 package (Wickham et al. 2022) in R (R Core Team 2020) to analyze and visualize stakeholders’ attributes.

Figure: Categorization of stakeholders to identify potential core experts and resource persons

Download the R code for expert identification in section 1. Plot stakeholder attributes: experience, availability, gender and expertise to identify experts

1.4. Cost-benefit analysis for each stakeholder

We used a dataset collected by Luu et al. (2022) and extracted the cost-benefit profile for all stakeholders who, according to the design, are expected to engage directly or be directly affected by the implementation of ACS

Figure: Net Present Values of four agro-climate service (ACS) interventions in Dien Bien District, Vietnam, aggregated for different stakeholders. Results were obtained through Monte Carlo simulation with 10,000 model runs for each investment scenario.

A summary of individual Net Present Value results for each stakeholder is available in the supporting material Supplementary Material 1.

An input table used for the analysis is available in the supporting document at Input table for cost-benefit analysis.

Download the R code for stakeholder cost benefit analysis.

1.5. Perceived interest, influence, relevance and attitude of stakeholders

We conducted three group discussions (FGDs) with the expert team in two years (i.e. 2019 and 2020) to map out the perceived attributes of stakeholders. These attributes included interest, influence, relevance and attitudes. We plotted the categorization of stakeholders in a four-dimensional matrix using the ggplot2 package (Wickham et al. 2022) in R (R Core Team 2020).

Below Figure is the result of the stakeholder analysis for 2019

Figure: Perceived interest, influence, relevance and attitude of stakeholders in the decision to scale agro-climate services in Dien Bien District, Vietnam. Results were captured through expert consultation in 2019

Below Figure is the result of the stakeholder analysis for 2020

Figure: Perceived interest, influence, relevance and attitude of stakeholders in the decision to scale agro-climate services in Dien Bien District, Vietnam. Results were captured through expert consultation in 2020

We used these analyses to develop recommendations for engaging these stakeholders in the relevant roles in the ACS budgeting and decision-making processes.

Download the R code for stakeholder analysis in Section 2. Stakeholder power and interest analysis in 2019 and Section 3. Stakeholder power and interest analysis in 2020.

1.6. Possible considerations in engaging stakeholders

Experts relied on the identified attributes of stakeholders and integrated such understanding into the administrative and nested budget system to suggest considerations to specifically engage stakeholders in the socio-economic development planning processes in Dien Bien.

Figure: Administrative structure and nested budget system in Vietnam. Adapted from Asian Development Bank (2017) and Strauch et al. (2018)

Detailed suggestions for the pathways to engage stakeholders are available in the Supplementary Material 2

Figure: Suggestions for the pathways to engage stakeholders. Adapted from Geels (2010)

1.7. Stakeholder input table

The input table stakeholder.csv contains variables used in the stakeholder analysis. The table includes the names and other attributes of stakeholders. Download the stakeholder input table.

2. Supplementary Materials

Supplementary Material 1: Summary of individual Net Present Value results for each stakeholder

Supplementary Material 2: Suggested pathways to integrate ACS in the SEDP and roles of institutional stakeholders

3. Acknowledgements

We acknowledge the valuable support from CARE in Vietnam and Dien Bien Centre of Community Development. We sincerely thank the Schlumberger Foundation for providing a scholarship for the main author through the Faculty for the Future Program. The views expressed in this research are those of the authors and do not necessarily reflect the views of CARE in Vietnam, Dien Bien Centre of Community Development or the Schlumberger Foundation.

3. References

Asian Development Bank. 2017. “Public Financial Management Systems—Viet Nam: Key Elements from a Financial Management Perspective.” Asian Development Bank. https://doi.org/10.22617/RPT178643-2.

Geels, Frank, and Johan Schot. 2010. “The Dynamics of Transitions: A Socio-Technical Perspective.” In Transitions to Sustainable Development: New Directions in the Study of Long-Term Transformative Change, 11–104. Routledge. https://www.routledge.com/Transitions-to-Sustainable-Development-New-Directions-in-the-Study-of-Long/Grin-Rotmans-Schot/p/book/9780415898041.

Luu, Thi Thu Giang, Cory Whitney, Lisa Biber-Freudenberger, and Eike Luedeling. 2022. “Decision Analysis of Agro-Climate Service Scaling – A Case Study in Dien Bien District, Vietnam.” Climate Services 27 (August): 100313. https://doi.org/10.1016/j.cliser.2022.100313.

Luu, Thi Thu Giang, Cory Whitney, and Eike Luedeling. 2022. “ThiThuGiangLuu/ACS-Decision-Analysis: Decision Analysis of Agro-Climate Scaling in Dien Bien, Vietnam. Zenodo, V1.2.” Zenodo. https://doi.org/10.5281/zenodo.6426967.

R Core Team. 2020. “R: A Language and Environment for Statistical Computing. Https://Www.R-Project.org/.” Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Reed, Mark S., Anil Graves, Norman Dandy, Helena Posthumus, Klaus Hubacek, Joe Morris, Christina Prell, Claire H. Quinn, and Lindsay C. Stringer. 2009. “Who’s in and Why? A Typology of Stakeholder Analysis Methods for Natural Resource Management.” Journal of Environmental Management 90 (5): 1933–49. https://doi.org/10.1016/j.jenvman.2009.01.001.

Strauch, Lisa, Robiou du Pont Yann, and Julia Balanowsk. 2018. “Multi-Level Climate Governance in Vietnam. Bridging National Planning and Local Climate Action.” Berlin: adelphi. https://www.adelphi.de/en/publication/multi-level-climate-governance-vietnam. https://www.adelphi.de/en/publication/multi-level-climate-governance-vietnam.

Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, Dewey Dunnington, and RStudio. 2022. “Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics.” https://CRAN.R-project.org/package=ggplot2.